Local Agentic Theory for Accessible Mobile Games [Testμ 2026]

At Testμ Conf 2026, New York Times Staff Game Engineer Shafik Quoraishee explored the promise and constraints of running agentic AI directly on devices for games and accessibility testing. Local inference can provide lower latency, privacy, offline operation, personalization, and local test execution, but must fit tightly coupled memory, 16-millisecond frame-time, and battery-energy budgets on hardware not primarily designed for continuous AI workloads. He distinguished reactive, tool-using agentic systems from reinforcement learning, illustrating an agentic perceive-predict-decide-act loop through a rebuilt Space Invaders game with discrete actions such as moving, evading, and shooting. The session discussed techniques for balancing resource constraints, the added potential and cost of gaze estimation and multiple on-device models, and current limitations in model reasoning and action capabilities. Quoraishee emphasized active accessibility testing as a practical application, where agents interact with live interfaces to identify dynamic WCAG problems such as focus traps that static code scans may miss, while noting that the work is exploratory and that New York Times puzzles remain human-made without AI features.

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